基于空谱加权近邻的高光谱图像分类算法 
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Hyperspectral Image Classification Algorithm Based on Space-Spectral Weighted Nearest Neighbor
1 贵州大学大数据与信息工程学院, 贵州 贵阳 550025
2 重庆大学光电工程学院, 重庆 400044
图 & 表
图 1. 过滤背景点过程。(a)原始图像;(b)随机样本点;(c)非近邻样本点;(d)处理非近邻样本;(e)过滤后的样本点
Fig. 1. Process of removing background point. (a) Original image; (b) random sample points; (c) non-nearest neighbor sample points; (d) processing non-nearest neighbor sample points; (e) filtered sample points
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图 2. Indian Pines数据集。(a)假彩色图;(b)地物类型调查图;(c)光谱曲线图[14]
Fig. 2. Indian Pines dataset. (a) False-color image; (b) ground-type survey map;(c) spectral curves[14]
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图 3. PaviaU数据集。(a)假彩色图;(b)地物类型调查图;(c)光谱曲线[14]
Fig. 3. PaviaU dataset. (a) False-color image; (b) ground-type survey map; (c) spectral curves[14]
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图 4. 不同窗口下Indian Pines数据集的OA
Fig. 4. OA of Indian Pines dataset with different spatial windows
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图 5. 不同训练样本比例下各算法的OA
Fig. 5. OA of different algorithms with different percentages of training samples
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图 6. 各算法在Indian Pines数据集的分类结果。(a) NN;(b) SRC;(c) SVM;(d) WSSD-KNN;(e) SSNN;(f) SSWNN
Fig. 6. Classification results of different algorithms in Indian Pines dataset. (a) NN; (b) SRC; (c) SVM; (d) WSSD-KNN; (e)SSNN; (f) SSWNN
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图 7. 不同窗口下PaviaU数据集的OA
Fig. 7. OA of PaviaU dataset with different spatial windows
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图 8. 不同算法在不同训练样本比例下的OA
Fig. 8. OA of different algorithms with different percentages of training samples
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图 9. 各算法在PaviaU数据及上的分类结果图。(a) NN;(b) SRC;(c) SVM;(d) WSSD-KNN;(e) SSNN;(f) SSWNN
Fig. 9. Classification results of different algorithms in PaviaU dataset. (a) NN; (b) SRC; (c) SVM; (d) WSSD-KNN; (e) SSNN; (f) SSWNN
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表 1Indian Pines数据集中各种算法在不同类别中的分类精度
Table1. Classification accuracy of different classes in Indian Pines dataset for different algorithms
| Grade | Category | Trainingsample set | Testsample set | Classification accuracy /% |
|---|
| NN | SRC | SVM | WSSD-KNN | SSNN | SSWNN |
|---|
| 1 | Alfalfa | 10 | 36 | 48.28 | 55.56 | 69.44 | 92.11 | 97.22 | 100.00 | | 2 | Corn-notill | 143 | 1285 | 58.86 | 55.98 | 72.13 | 89.13 | 94.17 | 98.24 | | 3 | Corn-min | 83 | 747 | 51.47 | 54.03 | 69.23 | 84.14 | 93.84 | 94.55 | | 4 | Corn | 24 | 213 | 44.15 | 41.40 | 57.61 | 82.76 | 88.00 | 99.00 | | 5 | Grass/Pasture | 48 | 435 | 85.30 | 82.37 | 86.71 | 97.03 | 100.00 | 98.82 | | 6 | Grass/Tress | 73 | 657 | 84.30 | 79.92 | 90.61 | 98.31 | 93.94 | 96.98 | | 7 | Grass Pasture mowed | 10 | 18 | 50.00 | 60.61 | 69.23 | 69.23 | 76.67 | 80.77 | | 8 | Hay-windrowed | 48 | 430 | 91.29 | 93.10 | 96.52 | 100.00 | 98.62 | 100.00 | | 9 | Oats | 10 | 10 | 16.67 | 14.29 | 38.46 | 62.50 | 65.22 | 63.16 | | 10 | Soybean-notill | 97 | 875 | 59.55 | 58.39 | 73.99 | 87.76 | 95.27 | 95.74 | | 11 | Soybean-min | 246 | 2209 | 69.40 | 69.54 | 81.82 | 92.36 | 95.29 | 96.02 | | 12 | Soybean-clean | 59 | 534 | 48.35 | 46.72 | 79.22 | 82.99 | 91.62 | 96.17 | | 13 | Wheat | 21 | 184 | 85.43 | 86.44 | 94.27 | 98.92 | 94.59 | 94.38 | | 14 | Woods | 127 | 1138 | 90.54 | 89.05 | 93.80 | 97.82 | 99.12 | 97.09 | | 15 | Buildings-Grass-Tree-Drives | 39 | 347 | 40.94 | 51.44 | 63.95 | 91.88 | 98.18 | 99.37 | | 16 | Stone-steel-towers | 10 | 83 | 96.34 | 98.68 | 98.65 | 98.80 | 93.24 | 94.74 | | OA | 68.70 | 68.71 | 80.66 | 91.74 | 95.31 | 96.75 | | AA | 63.80 | 64.84 | 77.23 | 89.11 | 92.19 | 94.06 | | Kappa | 0.643 | 0.642 | 0.780 | 0.906 | 0.947 | 0.963 |
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表 2PaviaU数据集中各类地物在不同算法下的分类精度
Table2. Classification accuracy of different classes in PaviaU dataset for different algorithms
| Grade | Category | Trainingsample set | Testsample set | Classification accuracy /% |
|---|
| NN | SRC | SVM | WSSD-KNN | SSNN | SSWNN |
|---|
| 1 | Asphalt | 398 | 6233 | 91.61 | 93.13 | 93.09 | 97.91 | 100.00 | 99.64 | | 2 | Meadows | 1119 | 17530 | 87.79 | 87.09 | 93.23 | 97.73 | 99.90 | 99.49 | | 3 | Gravel | 126 | 1973 | 65.98 | 65.74 | 84.46 | 96.51 | 61.76 | 97.11 | | 4 | Trees | 184 | 2880 | 94.34 | 94.97 | 95.35 | 99.57 | 98.77 | 97.82 | | 5 | Sheets | 81 | 1264 | 99.42 | 99.75 | 99.26 | 99.59 | 99.91 | 96.52 | | 6 | Soil | 302 | 4727 | 71.82 | 71.93 | 87.65 | 96.47 | 99.78 | 99.49 | | 7 | Bitumen | 80 | 1250 | 69.08 | 68.04 | 87.57 | 91.46 | 77.68 | 98.18 | | 8 | Bricks | 221 | 3461 | 65.22 | 66.95 | 79.19 | 93.68 | 96.08 | 93.59 | | 9 | Shadows | 57 | 890 | 99.75 | 99.88 | 99.40 | 99.65 | 96.13 | 96.34 | | OA | 83.83 | 83.91 | 91.24 | 97.21 | 95.57 | 98.54 | | AA | 82.78 | 83.05 | 91.02 | 96.95 | 92.22 | 97.57 | | Kappa | 0.783 | 0.784 | 0.883 | 0.963 | 0.941 | 0.981 |
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纪磊, 张欣, 张丽梅, 文章. 基于空谱加权近邻的高光谱图像分类算法[J]. 激光与光电子学进展, 2020, 57(6): 061013. Lei Ji, Xin Zhang, Limei Zhang, Zhang Wen. Hyperspectral Image Classification Algorithm Based on Space-Spectral Weighted Nearest Neighbor[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061013.
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